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Machine Fault Diagnostics and Prognostics II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 26754

Special Issue Editors


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Guest Editor
School of Computer Science and Engineering, Soongsil University, Seoul 06978, Korea
Interests: fault diagnostics; health prognosis; mobile system design; machine learning; edge computing; embedded system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are currently living through the fourth Industrial revolution, which is riding on the wave of cutting-edge technologies in computing, artificial intelligence, and communications. The past decade has witnessed incredible advances in the field of artificial intelligence (AI) and has seen a massive proliferation of cloud computing technologies. These technological advances have further fueled the integration of the cyber and the physical worlds, with intelligence and autonomy as its key hallmarks, which would lead to more reliable, productive, and efficient industries and businesses in the future.

Machines and mechanical structures in industries undergo inevitable degradation and loss of performance during operation. The timely diagnosis of symptoms of their degradation and a reliable estimate of their future health condition are essential for industrial productivity and reliability. Models constructed from historical measurement data using AI techniques have shown great promise in fault diagnosis and prognosis of industrial equipment. AI-based techniques are poised to gain even more significance in the future as huge amounts of measurement data are to be available for decision making due to the deployment of the Internet of Things and cloud-based technologies for condition-based maintenance (CBM).

This Special Issue will focus on the topic of fault diagnosis and prognosis of industrial equipment and mechanical structures. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to condition-based monitoring, fault diagnosis and prognosis of industrial machines and mechanical structures, diagnostic and prognostic techniques based on AI, such as deep learning, transfer learning, and neuro-fuzzy inference techniques, AI-based solutions that are explainable, solutions utilizing the Internet of Things, cloud computing, cyberphysical systems, and machine-to-machine interfaces and paradigms for fault diagnosis and prognosis in the context of Industry 4.0. We would also welcome review articles that capture the current state-of-the art and outline future areas of research in the fields relevant to this Special Issue.

Prof. Dr. Jong-Myon Kim
Prof. Dr. Cheol Hong Kim
Guest Editors

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Keywords

  • Condition monitoring
  • Fault diagnosis
  • Health prognosis
  • Remaining useful life
  • Deep learning
  • Artificial intelligence
  • Condition-based maintenance
  • Cyberphysical systems

Published Papers (10 papers)

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Research

13 pages, 1947 KiB  
Article
A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
by Ke Zheng, Guozhu Jia, Linchao Yang and Chunting Liu
Appl. Sci. 2021, 11(23), 11116; https://doi.org/10.3390/app112311116 - 23 Nov 2021
Cited by 6 | Viewed by 1479
Abstract
In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible [...] Read more.
In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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26 pages, 12486 KiB  
Article
Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network
by Cong Dai Nguyen, Zahoor Ahmad and Jong-Myon Kim
Appl. Sci. 2021, 11(16), 7575; https://doi.org/10.3390/app11167575 - 18 Aug 2021
Cited by 13 | Viewed by 2442
Abstract
This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful [...] Read more.
This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is often overwhelmed by random interference noises. Furthermore, the varying speed of gearboxes makes it difficult to distinguish the fault-related frequencies from the interference noises. To obtain a noise-free signal for extraction of fault-related information under variable speed conditions, first, a new localized adaptive denoising technique (LADT) is applied to the vibration signal. The new localized adaptive denoising technique results in optimized vibration sub-bands with negligible background noise. To obtain fault-related information, the wavelet-based vibration imaging approach (WVI) is applied to the denoised vibration signal. The wavelet-based vibration imaging approach decomposes the vibration signal into different time–frequency scales, these scales are reflected by a two-dimensional image called a scalogram. The scalograms obtained from the wavelet-based vibration imaging approach are provided as an input to the deep convolutional neural network architecture (DCNA) for extraction of discriminant features and classification of multi-degree tooth faults (MDTFs) in a gearbox under variable speed conditions. The proposed scheme outperforms the already existing state-of-the-art gearbox fault diagnosis methods with the highest classification accuracy of 100%. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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16 pages, 6325 KiB  
Article
Health Indicators Construction and Remaining Useful Life Estimation for Concrete Structures Using Deep Neural Networks
by Viet Tra, Tuan-Khai Nguyen, Cheol-Hong Kim and Jong-Myon Kim
Appl. Sci. 2021, 11(9), 4113; https://doi.org/10.3390/app11094113 - 30 Apr 2021
Cited by 6 | Viewed by 1959
Abstract
Remaining useful life (RUL) prognosis is one of the most important techniques in concrete structure health management. This technique evaluates the concrete structure strength through determining the advent of failure, which is very helpful to reduce maintenance costs and extend structure life. Degradation [...] Read more.
Remaining useful life (RUL) prognosis is one of the most important techniques in concrete structure health management. This technique evaluates the concrete structure strength through determining the advent of failure, which is very helpful to reduce maintenance costs and extend structure life. Degradation information with the capability of reflecting structure health can be considered as a principal factor to achieve better prognosis performance. In traditional data-driven RUL prognosis, there are drawbacks in which features are manually extracted and threshold is defined to mark the specimens breakdown. To overcome these limitations, this paper presents an innovative SAE-DNN structure capable of automatic health indicator (HI) construction from raw signals. HI curves constructed by SAE-DNN have much better fitness metrics than HI curves constructed from statistical parameters such as RMS, Kurtosis, Sknewness, etc. In the next stage, HI curves constructed from training degradation data are then used to train a long short-term memory recurrent neural network (LSTM-RNN). The LSTM-RNN is utilized as a RUL predictor since its special gates allow it to learn long-term dependencies even when the training data is limited. Model construction, verification, and comparison are performed on experimental reinforced concrete (RC) beam data. Experimental results indicates that LSTM-RNN generally estimates more accurate RULs of concrete beams than GRU-RNN and simple RNN with the average prediction error cycles was less than half compared to those of the simple RNN. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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19 pages, 3679 KiB  
Article
A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence
by Ge Zhang, Qiong Yang, Guotong Li, Jiaxing Leng and Long Wang
Appl. Sci. 2021, 11(2), 797; https://doi.org/10.3390/app11020797 - 15 Jan 2021
Cited by 4 | Viewed by 1729
Abstract
Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in [...] Read more.
Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in the context of detecting incipient faults in satellites. Under the assumption that the variables obey a multidimensional Gaussian distribution and using KL divergence to detect incipient faults, this paper models the optimum PV for detecting incipient faults as an optimization problem. It proves that the PVs obtained by principal component analysis (PCA) are not necessarily the optimum PV for detecting incipient faults. It then compares the on-line probability density function (PDF) with the reference PDF for detecting incipient faults on the local optimum PV. A numerical example and a real satellite fault case were used to assess the validity and superiority of the method proposed in this paper over conventional methods. Since the method takes into account the characteristics of the actual incipient faults, it is more adaptable to various possible incipient faults. Fault detection rates of three simulated faults and the real satellite fault are 98%, 84%, 93% and 92%, respectively. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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18 pages, 3308 KiB  
Article
A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
by Bach Phi Duong, Jae Young Kim, Inkyu Jeong, Kichang Im, Cheol Hong Kim and Jong Myon Kim
Appl. Sci. 2020, 10(24), 8800; https://doi.org/10.3390/app10248800 - 9 Dec 2020
Cited by 22 | Viewed by 3139
Abstract
A new method is established to construct the 2-D fault diagnosis representation of multiple bearing defects from 1-D acoustic emission signals. This technique starts by applying envelope analysis to extract the envelope signal. A novel strategy is propounded for the deployment of the [...] Read more.
A new method is established to construct the 2-D fault diagnosis representation of multiple bearing defects from 1-D acoustic emission signals. This technique starts by applying envelope analysis to extract the envelope signal. A novel strategy is propounded for the deployment of the continuous wavelet transform with damage frequency band information to generate the defect signature wavelet image (DSWI), which describes the acoustic emission signal in time-frequency-domain, reduces the nonstationary effect in the signal, shows discriminate pattern visualization for different types of faults, and associates with the defect signature of bearing faults. Using the resultant DSWI, the deep convolution neural network (DCNN) architecture is designed to identify the fault in the bearing. To evaluate the proposed algorithm, the performance of this technique is scrutinized by a series of experimental tests acquired from a self-designed testbed and corresponding to different bearing conditions. The performance from the experimental dataset demonstrates that the suggested methodology outperforms conventional approaches in terms of classification accuracy. The result of combining the DCNN with DSWI input yields an accuracy of 98.79% for classifying multiple bearing defects. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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14 pages, 5515 KiB  
Article
Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions
by Minh Tuan Pham, Jong-Myon Kim and Cheol Hong Kim
Appl. Sci. 2020, 10(20), 7068; https://doi.org/10.3390/app10207068 - 12 Oct 2020
Cited by 26 | Viewed by 3018
Abstract
Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency [...] Read more.
Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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14 pages, 4258 KiB  
Article
Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram
by Minh Tuan Pham, Jong-Myon Kim and Cheol Hong Kim
Appl. Sci. 2020, 10(18), 6385; https://doi.org/10.3390/app10186385 - 13 Sep 2020
Cited by 57 | Viewed by 4462
Abstract
Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful [...] Read more.
Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful information from vibration signals, diagnosis of motor failures by maintenance engineers can be gradually replaced by an automatic detection process. Especially, state-of-the-art methods using deep learning have contributed significantly to automatic fault diagnosis. This paper proposes a novel method for diagnosing bearing faults and their degradation level under variable shaft speed. In the proposed method, vibration signals are represented by spectrograms to apply deep learning methods through preprocessing using Short-Time Fourier Transform (STFT). Then, feature extraction and health status classification are performed by a convolutional neural network (CNN), VGG16. According to our various experiments, our proposed method can achieve very high accuracy and robustness for bearing fault diagnosis even under noisy environments. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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16 pages, 2512 KiB  
Article
Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission
by Hanxue Zhao, Zhenlin Li, Shenbin Zhu and Ying Yu
Appl. Sci. 2020, 10(16), 5544; https://doi.org/10.3390/app10165544 - 11 Aug 2020
Cited by 11 | Viewed by 2213
Abstract
Valve internal leakage is easily found because of various defects resulting from environmental factors and load fluctuation. The timely detection of valve internal leakage is of great significance to the safe operation of pipelines. As an effective means for detecting valve internal leakage, [...] Read more.
Valve internal leakage is easily found because of various defects resulting from environmental factors and load fluctuation. The timely detection of valve internal leakage is of great significance to the safe operation of pipelines. As an effective means for detecting valve internal leakage, the acoustic emission technique is characterized by nonintrusive and strong anti-interference ability, which can realize the in situ monitoring of the valve running status in real time. In this paper, acoustic emission signals from an internal leaking valve were obtained experimentally. Then, the dimensionality reduction technology based on factor analysis was introduced to the processing of valve internal leakage detection data. Next, the wavelet decomposition was carried out to decompose the sample feature set into four subsets. Finally, the decomposed sample feature sets were inputted into the error backpropagation (BP) neural network quantitative model, respectively. The optimized results show that the predicted internal leakage rate by the wavelet-BP neural network model has good precision with an error of less than 10%. The wavelet-BP neural network model can realize the analysis of the valve internal leakage rate quantitatively and has good robustness, which provides technical support and guarantees the safe operation of natural gas pipeline valves. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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20 pages, 11677 KiB  
Article
Study on the Fault Diagnosis Method of Scraper Conveyor Gear under Time-Varying Load Condition
by Shuanfeng Zhao, Pengfei Wang and Shijun Li
Appl. Sci. 2020, 10(15), 5053; https://doi.org/10.3390/app10155053 - 23 Jul 2020
Cited by 16 | Viewed by 2456
Abstract
Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a [...] Read more.
Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a fault-diagnosis method without sensor installation, which is easier to realize in the mine. Therefore, a fault diagnosis method for local gear fault, which is based on bispectral analysis (BA) of analytical signal envelope obtained by processing a stator current under time-varying load condition, is proposed in our paper. In this method, the fault frequency component is enhanced by eliminating the interference of fundamental frequency and coal flow impact. Then, the enhanced fault frequency component is extracted by BA, and a quantitative analysis of the fault strength under time-varying load is carried out from the perspective of energy. Finally, the proposed method is verified on the number HB-kpl-75 scraper conveyor reducer, and the results show that this method can successfully diagnose the failure of the scraper conveyor gear under time-varying load conditions. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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14 pages, 921 KiB  
Article
Feature Selection for Improving Failure Detection in Hard Disk Drives Using a Genetic Algorithm and Significance Scores
by Wasim Ahmad, Sheraz Ali Khan, Cheol Hong Kim and Jong-Myon Kim
Appl. Sci. 2020, 10(9), 3200; https://doi.org/10.3390/app10093200 - 4 May 2020
Cited by 5 | Viewed by 2723
Abstract
Hard disk drives (HDD) are used for data storage in personal computing platforms as well as commercial datacenters. An abrupt failure of these devices may result in an irreversible loss of critical data. Most HDD use self-monitoring, analysis, and reporting technology (SMART), and [...] Read more.
Hard disk drives (HDD) are used for data storage in personal computing platforms as well as commercial datacenters. An abrupt failure of these devices may result in an irreversible loss of critical data. Most HDD use self-monitoring, analysis, and reporting technology (SMART), and record different performance parameters to assess their own health. However, not all SMART attributes are effective at detecting a failing HDD. In this paper, a two-tier approach is presented to select the most effective precursors for a failing HDD. In the first tier, a genetic algorithm (GA) is used to select a subset of SMART attributes that lead to easily distinguishable and well clustered feature vectors in the selected subset. The GA finds the optimal feature subset by evaluating only combinations of SMART attributes, while ignoring their individual fitness. A second tier is proposed to filter the features selected using the GA by evaluating each feature independently, using a significance score that measures the statistical contribution of a feature towards disk failures. The resultant subset of selected SMART attributes is used to train a generative classifier, the naïve Bayes classifier. The proposed method is tested on a SMART dataset from a commercial datacenter, and the results are compared with state-of-the-art methods, indicating that the proposed method has a better failure detection rate and a reasonable false alarm rate. It uses fewer SMART attributes, which reduces the required training time for the classifier and does not require tuning any parameters or thresholds. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)
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